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Int J Chron Obstruct Pulmon Dis. 2017 Nov 1;12:3183-3194. doi: 10.2147/COPD.S142378. eCollection 2017.

Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources.

Author information

Institute for Medical Technology Assessment (iMTA)/Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, the Netherlands.
Department for Prevention and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Department of Epidemiology, Groningen University, University Medical Centre Groningen, Groningen, the Netherlands.
Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.
Health Economics Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine, The OLIN Unit, Umeå University, Umeå, Sweden.
ICON Health Economics, Toronto, Canada.
Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD, USA.


Background and objectives:

Exacerbations are important outcomes in COPD both from a clinical and an economic perspective. Most studies investigating predictors of exacerbations were performed in COPD patients participating in pharmacological clinical trials who usually have moderate to severe airflow obstruction. This study was aimed to investigate whether predictors of COPD exacerbations depend on the COPD population studied.


A network of COPD health economic modelers used data from five COPD data sources - two population-based studies (COPDGene® and The Obstructive Lung Disease in Norrbotten), one primary care study (RECODE), and two studies in secondary care (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoint and UPLIFT) - to estimate and validate several prediction models for total and severe exacerbations (= hospitalization). The models differed in terms of predictors (depending on availability) and type of model.


FEV1% predicted and previous exacerbations were significant predictors of total exacerbations in all five data sources. Disease-specific quality of life and gender were predictors in four out of four and three out of five data sources, respectively. Age was significant only in the two studies including secondary care patients. Other significant predictors of total exacerbations available in one database were: presence of cough and wheeze, pack-years, 6-min walking distance, inhaled corticosteroid use, and oxygen saturation. Predictors of severe exacerbations were in general the same as for total exacerbations, but in addition low body mass index, cardiovascular disease, and emphysema were significant predictors of hospitalization for an exacerbation in secondary care patients.


FEV1% predicted, previous exacerbations, and disease-specific quality of life were predictors of exacerbations in patients regardless of their COPD severity, while age, low body mass index, cardiovascular disease, and emphysema seem to be predictors in secondary care patients only.


COPD; exacerbations; hospitalizations; modeling; validation

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